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Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data
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| Zeitschriftentitel: | Genes |
|---|---|
| Personen und Körperschaften: | , , , , , , , |
| In: | Genes, 10, 2019, 3, S. 200 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
MDPI AG
|
| Schlagwörter: |
| author_facet |
Tao, Mingxin Song, Tianci Du, Wei Han, Siyu Zuo, Chunman Li, Ying Wang, Yan Yang, Zekun Tao, Mingxin Song, Tianci Du, Wei Han, Siyu Zuo, Chunman Li, Ying Wang, Yan Yang, Zekun |
|---|---|
| author |
Tao, Mingxin Song, Tianci Du, Wei Han, Siyu Zuo, Chunman Li, Ying Wang, Yan Yang, Zekun |
| spellingShingle |
Tao, Mingxin Song, Tianci Du, Wei Han, Siyu Zuo, Chunman Li, Ying Wang, Yan Yang, Zekun Genes Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data Genetics (clinical) Genetics |
| author_sort |
tao, mingxin |
| spelling |
Tao, Mingxin Song, Tianci Du, Wei Han, Siyu Zuo, Chunman Li, Ying Wang, Yan Yang, Zekun 2073-4425 MDPI AG Genetics (clinical) Genetics http://dx.doi.org/10.3390/genes10030200 <jats:p>It is very significant to explore the intrinsic differences in breast cancer subtypes. These intrinsic differences are closely related to clinical diagnosis and designation of treatment plans. With the accumulation of biological and medicine datasets, there are many different omics data that can be viewed in different aspects. Combining these multiple omics data can improve the accuracy of prediction. Meanwhile; there are also many different databases available for us to download different types of omics data. In this article, we use estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) to define breast cancer subtypes and classify any two breast cancer subtypes using SMO-MKL algorithm. We collected mRNA data, methylation data and copy number variation (CNV) data from TCGA to classify breast cancer subtypes. Multiple Kernel Learning (MKL) is employed to use these omics data distinctly. The result of using three omics data with multiple kernels is better than that of using single omics data with multiple kernels. Furthermore; these significant genes and pathways discovered in the feature selection process are also analyzed. In experiments; the proposed method outperforms other state-of-the-art methods and has abundant biological interpretations.</jats:p> Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data Genes |
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| title |
Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data |
| title_unstemmed |
Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data |
| title_full |
Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data |
| title_fullStr |
Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data |
| title_full_unstemmed |
Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data |
| title_short |
Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data |
| title_sort |
classifying breast cancer subtypes using multiple kernel learning based on omics data |
| topic |
Genetics (clinical) Genetics |
| url |
http://dx.doi.org/10.3390/genes10030200 |
| publishDate |
2019 |
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200 |
| description |
<jats:p>It is very significant to explore the intrinsic differences in breast cancer subtypes. These intrinsic differences are closely related to clinical diagnosis and designation of treatment plans. With the accumulation of biological and medicine datasets, there are many different omics data that can be viewed in different aspects. Combining these multiple omics data can improve the accuracy of prediction. Meanwhile; there are also many different databases available for us to download different types of omics data. In this article, we use estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) to define breast cancer subtypes and classify any two breast cancer subtypes using SMO-MKL algorithm. We collected mRNA data, methylation data and copy number variation (CNV) data from TCGA to classify breast cancer subtypes. Multiple Kernel Learning (MKL) is employed to use these omics data distinctly. The result of using three omics data with multiple kernels is better than that of using single omics data with multiple kernels. Furthermore; these significant genes and pathways discovered in the feature selection process are also analyzed. In experiments; the proposed method outperforms other state-of-the-art methods and has abundant biological interpretations.</jats:p> |
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| author | Tao, Mingxin, Song, Tianci, Du, Wei, Han, Siyu, Zuo, Chunman, Li, Ying, Wang, Yan, Yang, Zekun |
| author_facet | Tao, Mingxin, Song, Tianci, Du, Wei, Han, Siyu, Zuo, Chunman, Li, Ying, Wang, Yan, Yang, Zekun, Tao, Mingxin, Song, Tianci, Du, Wei, Han, Siyu, Zuo, Chunman, Li, Ying, Wang, Yan, Yang, Zekun |
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| description | <jats:p>It is very significant to explore the intrinsic differences in breast cancer subtypes. These intrinsic differences are closely related to clinical diagnosis and designation of treatment plans. With the accumulation of biological and medicine datasets, there are many different omics data that can be viewed in different aspects. Combining these multiple omics data can improve the accuracy of prediction. Meanwhile; there are also many different databases available for us to download different types of omics data. In this article, we use estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) to define breast cancer subtypes and classify any two breast cancer subtypes using SMO-MKL algorithm. We collected mRNA data, methylation data and copy number variation (CNV) data from TCGA to classify breast cancer subtypes. Multiple Kernel Learning (MKL) is employed to use these omics data distinctly. The result of using three omics data with multiple kernels is better than that of using single omics data with multiple kernels. Furthermore; these significant genes and pathways discovered in the feature selection process are also analyzed. In experiments; the proposed method outperforms other state-of-the-art methods and has abundant biological interpretations.</jats:p> |
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| spelling | Tao, Mingxin Song, Tianci Du, Wei Han, Siyu Zuo, Chunman Li, Ying Wang, Yan Yang, Zekun 2073-4425 MDPI AG Genetics (clinical) Genetics http://dx.doi.org/10.3390/genes10030200 <jats:p>It is very significant to explore the intrinsic differences in breast cancer subtypes. These intrinsic differences are closely related to clinical diagnosis and designation of treatment plans. With the accumulation of biological and medicine datasets, there are many different omics data that can be viewed in different aspects. Combining these multiple omics data can improve the accuracy of prediction. Meanwhile; there are also many different databases available for us to download different types of omics data. In this article, we use estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) to define breast cancer subtypes and classify any two breast cancer subtypes using SMO-MKL algorithm. We collected mRNA data, methylation data and copy number variation (CNV) data from TCGA to classify breast cancer subtypes. Multiple Kernel Learning (MKL) is employed to use these omics data distinctly. The result of using three omics data with multiple kernels is better than that of using single omics data with multiple kernels. Furthermore; these significant genes and pathways discovered in the feature selection process are also analyzed. In experiments; the proposed method outperforms other state-of-the-art methods and has abundant biological interpretations.</jats:p> Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data Genes |
| spellingShingle | Tao, Mingxin, Song, Tianci, Du, Wei, Han, Siyu, Zuo, Chunman, Li, Ying, Wang, Yan, Yang, Zekun, Genes, Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data, Genetics (clinical), Genetics |
| title | Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data |
| title_full | Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data |
| title_fullStr | Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data |
| title_full_unstemmed | Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data |
| title_short | Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data |
| title_sort | classifying breast cancer subtypes using multiple kernel learning based on omics data |
| title_unstemmed | Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data |
| topic | Genetics (clinical), Genetics |
| url | http://dx.doi.org/10.3390/genes10030200 |